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IT (ITOps) operations are always rooted in data collection and analysis. Artificial intelligence (AI) and machine learning (ML) are being applied to allow a new class of Ops tools to actually learn and improve from the data they collect. Advances are not coming too soon, as the IT crisis created by the COVID-19 pandemic forced organizations to stand up for widely distributed applications and infrastructure. The emerging layer of ITOps tool called AIOps promises a solution to sudden complexity.
David Linthicum, in his recently published report “Best practices in moving from ITOps to AIOps”, explores the journey IT organizations face as they seek to leverage ML and interact. Autonomous systems to speed diagnostics, reduce downtime, optimize infrastructure and predict challenges.
Linthicum divides this journey into four phases: ITOps, Emerging AIOps, Advanced AIOps and Future AIOps. This process begins with a traditional approach built around IT monitoring, automating scripts and Manual Operations processes, and ends with a process-driven, automated workflow. business prediction and automation.
“Note that we shifted from the limitations of the traditional approach to using the emerging AI Activity,” writes Linthicum in the report, citing the infographic in Figure 1. “This has some core attributes, such as the ability to monitor systems using correlated data, automate the manual, the ability the AI engine learns from the data, and the ability to deliver the bulk of the data. This function is on demand, according to the needs of the Ops groups. “
Figure 1: Stages of applying AIOps
Ultimately, the goal is to apply the concepts of automated computing, which deal with the self-managed properties of distributed computing resources and their ability to adapt to unpredictable changes in when hiding complexity from both the operator and the user. In other words, as Linthicum notes, “the ability to remove humans from the basic operational complexity.”
In the report, Linthicum offers best practices to help IT organizations embark on the AIOps journey. Tutorials begin with planning and measurement issues – review of business problems, course mapping to achieve AIOps, and enabled value processing. From there, he explored motion: Transforming into advanced concepts like predictive analytics and self-sufficiency while implementing a continual improvement process for AIOps and ensuring integration with other Ops tools. Ultimately, he urges introspection, value evaluation, and performance classification, and adopting a continuous cycle of the AIOps effort is going on.
As Linthicum observes, the use of the operational automation tool is a “must-have conclusion”, but that doesn’t mean it will come in time to address the facility’s soaring complexity. IT infrastructure. He urges organizations to map their AIOps journey early as a way to avoid being surprised.
Learn more: Best practices for switching from ITOps to AIOps